Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task.
In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES.
Deep learning models are extensively used in various safety critical applications.
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs.
Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios.